Effect of Milk Yield on Relationship Between Bulk Milk Somatic Cell Count and Prevalence of Mastitis ULF EMANUELSON AND HANS FUNKE

Swedsh Association for UV9stock Breeding and Production 5-631 84 Eskilstuna, Sweden

age of BMC, PREY = herd prevalence of mastitis, SJB = Swedish Jersey cattle, SKB = Swedish Polled cattle, SLB = Swedish Friesian cattle, SRB = Swedish Red and White cattle.

ABSTRACT

The possible dilution effect of increasing milk yield on bulk milk see was studied in a field trial. Data on breed distribution, numbers of cows, average milk yields, average bulk milk see, and estimated prevalences of mastitis were available for 15,514 Swedish dairy herds. The overall mean of herd prevalence of mastitis, as estimated by the defmition employed, was 26.7%, and the overall mean of the geometric average of bulk milk see was 204,000 cells/ml Correlations between prevalence of mastitis and average bulk milk see ranged between.53 and .77, and geometric averages were only marginally more correlated to prevalence of mastitis than were arithmetic averages. The average herd prevalence of mastitis was found to increase, within bulk milk see level, as milk. production increased. The regression coefficients of average milk yield on bulk milk see, estimated conditionally on mastitis prevalence, show that the bulk milk. see decreased by 11.1 % for each increase in the milk yield of 1000 kg of FCM. This implies that part of the decrease in average bulk milk SCC achieved during recent years may be an artifact due to the concurrent increase in milk. production. (Key words: cell count, milk yield, mastitis)

INTRODUCTION

Abbreviation key: BMC = bulk milk somatic cell count, BMCA = arithmetic average of BMC, BMCAW = weighted arithmetic average of BMC, BMCG = geometric average of BMC, BMCGW = weighted geometric aver-

ReceiVed Novembes- 20, 1990. Accepted April 17, 1991. 1991 J Dairy Sci 74:2479-2483

The bulk milk SCC (BMC) has been used for more than 30 yr as an indicator of udder health in dairy herds. In mastitis control programs in several countries, the BMC is used to indicate health status and also to monitor the progress achieved. Furthennore, in many countries the BMC is used as one of the measurement factors for milk payments to producers. The degree of association between BMC and prevalence of mastitis is, therefore, an important parameter, and considerable research has been Wldertaken in order to estimate it. For instance, correlation coefficients have demonstrated significant associations, but the estimates vary considerably; values range from .46 to .96 (8, 10, 11, 14, 15, 16, 18). Others have represented the association as average mastitis frequencies within cell count level, but the associated ranges have sometimes overlapped (2,6, 7, 10, 12). Factors of a pathological (17, 18) as well as a nonpathological (1, 3, 5. 9. 11, 18) nature have been discussed as possible causes of this variance in the estimated associations. The purpose of the present investigation was to study the possible effect of average milk yield on the correlation between BMC level and the prevalence of mastitis (pREY) using available data from the Swedish milk recording scheme. MATERIALS AND METHODS

All herds registered with the official milk recording scheme in Sweden during the period September 1988 to August 1989, totaling 15,514, were eligible for inclusion in the study. The data available on each herd included information on breed distribution, aver-

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TABLE 1. Overall means, standard deviations, median (MED), fust percentile (1%), and 99th percentile (99%) for herd average of kilograms of FCM, herd mean prevalence (percenIage) of mastitis (pREV), geometric mean (BMCG), weighted geomelric mean (BMCGW), arithmetic mean (BMCA), and weighted arithmetic mean (BMCAW) of bulk mille sec x 1000 per milliliter (number of herds = 15514). Variable

X

SD

MFD

FCM PREV BMCG BMCGW BMCA BMCAW

6501

883 10.2

6564 26.1 186 182 208 206

26.7 204 199 227 224

97 94 lOS

104

age number of cows (total number of cow-days divided by 365), average yield of FCM, average BMC, and PREV. Data was also available from the preceding year, September 1987 to August 1988, representing 15,802 herds, but results are only presented in full for the last year. Average BMC was based on bulk milk samples, analyzed for SCC once every month. Four averages were calculated: a geometric mean (BMCG), a geometric mean weighted according to the milk volume at sampling (BMCGW), an arithmetic mean (BMCA), and a weighted arithmetic mean (BMCAW). The PREV was calculated as the average of the lactational prevalence of mastitis for each cow in the herd. The lactational prevalence (P) was estimated according to Brolund's (4) formula: P = ~o + ~IC + ~C2 + ~3C3 where ~i are regression coefficients and C is the lactational average of the monthly SCC. This calculation gives an indirect estimation of the proportion of monthly tests that can be characterized as "infectious mastitis" (i.e., bacteria present and elevated SCC in at least one quarter) according to Brolund (3, 4). Regression coefficients were estimated by Brolund (3) on a material with more than 40,000 monthly quarter foremilk samples, with recorded bacteriological findings and SCC, and more than 10,000 monthly udder total milk sec (regression coefficients are available upon request). Ordinary least squares analysis of variance was applied in order to investigate the effect of milk yield on BMC. Initial analyses indicated that the effect of milk yield on BMC varied with BMC level, suggesting a multiplicative model rather than an additive one. Average BMC were, therefore, transformed to a comJournal of Dairy Science Vol.

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1% 4053 6.0 56 55 65 63

99% 8480 52.4 514 493 564 552

mon logarithmic scale. The model considered appropriate for describing the association between BMC and milk production was log (BMCG) = area + FCM + breed + ncow + area x ncow + PREY (area) + PREV2 (area) where area was a categorical variable representing seven geographical regions; FCM was the herd average of kilograms of FCM; ncow was a categorical variable with six classes representing the average number of cows in the herd (10 to 14, 15 to 19,20 to 24, 25 to 49, 50 to 74, and >74 cows, respectively); and breed was a categorical variable with five classes representing the predominant breed [>90% purebred Swedish Red and White (SRB), >90% purebred Swedish Friesian (SLB), >90% purebred Swedish Jersey (SJB), >90% purebred Swedish Polled (SKB), and all other combinations, respectively]. All two-factor interactions not included in the final model were found in preliminary analyses to be nonsignificant (P > .05). Only herds with an average number of cows exceeding nine were included in this analysis. The contribution of each effect in the statistical model to the total variation in log (BMCG) was calculated as the relative difference in mean square residual (MSR) between the full model and models in which one effect at a time was ignored: MSR (reduced model) - MSR (full model)1 MSR (reduced model) x 100. All statistical analyses were performed using procedures in SAS (13). RESULTS

Some general descriptive statistics for the herd average of kilograms of FCM, PREV, and

TABLE 2. Correlations between herd mean prevalence of mastitis (€‘REV) and geome& mean (BMCG), migeometric mean (BMCGW), arithmetic mean (BMCA), and weighted arithmetic mean (BMCAW) of bulk milk SCC X loo0 per milliliter for different herd sips (n = number of herds). Cornlation between PREV and

Herd size Number of cows 1-9 10-14 15-19 20-24

25-49 10-74 >74

n

BMCG

BMCGW

BMCA

BMCAW

1058 2314 3087 2863

548

546

527

.525

.617 .630 .639

.600

5306 651

.MIS

.618 .633 .639 .669 .733 .717

.595 .609 .618 .653 .726 .709

235

.734 .712

the different BMC averages are given in Table 1. The proportions of herds falling in the different breed classes were: 26.6, 17.1, .3, and .3% for herds with predominantly SRB, SLB, SJB, and SKB, respectively. The remaining herds (55.7%) had either cows of different breeds or crossbred cows. The proportions of herds in the different herd size categories ( 4 0 , 10 to 14,15 to 19,20 to 24,25 to 49,50 to 74, and >74 cows) were 6.8, 14.9, 19.9, 18.5,34.2, 4.2, and 1.5%. respectively. Correlations between PREW and the different BMC averages were calculated for different herd sizes (Table 2). All correlations proved highly significant (P c .001), and there was a tendency for correlations to increase with increasing herd size. The correlation between PREV and the arithmetic means (BMCA and BMCAW) was slightly smaller than that between PREV and the geometric means, but this difference decreased with increasing herd size.

.609 .621 .a2 .724 .705

Table 3 shows PREV at different average BMCG for three levels of production. The PREV increased, quite logically, with increasing BMCG level. The PREV also increased within BMCG level as milk production increased, thus indicating a dilution effect on BMC. Results from the analysis of variance are presented in Table 4 . All effects in the final model were highly significant, statistically, and the coefficient of determination was reasonably high (R2= 51.6%). The significance was partly a result of the large number of records included in the analysis. An alternate way to determine the relative impoxtance of each factor is also presented in Table 4. The most important factor contributing to the variation in log(BMCG) was PREV (the joint effect of PREV and P e ) , which accounted for about 43% of the total variation when all other effects were taken into account. The herd average milk yield was also a major source of

TABLE 3.Overall means for prevalence (percentage) of mastitis at d3Yerent average buk milk SCC x loo0 per milliliter (BMCG) according to selected levels of milk prodaction (kilograms of pcM). BMCG

5m

SD

. . .1 115 8.1 15.6 7.3 21.9 9.2 31.4 9.9 392 9.5

5500-6499

27500

SD 9.5 6.0 15.0 7.1 20.2 7.0 26.0 7.8 33.9 8.5 44.7 75

E

SD

15.2 17.7 23.4 30.1 39.8 48.8

4.1 6.7 7.0 7.7 8.6 10.0

INO herds in this combination of BMCG and level of mil^ production.

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sec

x 1000 per milliliter (BMCG).

Mean

Contribution to variation2

TABLE 4. Results of analysis of variance of log average bulk milk Source of variation 1

squares

.1). The data from the preceding year was very similar to the data presented. Average milk yield was marginally lower (6500 kg of FCM), and PREV was the same (26.7%) in this older data set compared with the newer data set. Correlations between PREY and the different means of BMC were considerably higher (.731 to .841), as was also the coefficient of detennination in the analysis of variance (R2 = 76.5%). However, exactly the same patterns were seen in this material: the correlations between PREY and BMC increased with increasing herd size; the correlations between PREY and the arithmetic mean (weighted) were slightly smaller than the corresponding correlations with geometric means, but this difference decreased with increasing herd size; PREY increased with increasing BMCG and also increased, within BMCG level, with increasing level of milk. production. Moreover, the estimated regression coefficient of kilograms of FCM on 10g(BMCG) was similar to the one presented (-5.82 x 10-5 , SE = .14 x

10-5).

Iournal of Dairy Science Vol. 74, No.8, 1991

DISCUSSION

The results presented show clearly that BMC is affected by milk yield when estimated conditionally on the mastitis prevalence (fables 3 and 4); Le., there is a dilution effect. The estimated regression of milk. yield on 10g(BMCG) means, when transfonned to the nonnal scale, that the BMCG decreases by 11.1% for an increase in milk yield of 1000 kg of FCM The relative decrease in BMCG is the same regardless of whether the increase in milk yield is from 3000 kg or from 7000 kg because quadratic and cubic regression coefficients were found to be nonsignificant. A hypothetical dilution effect can be readily calculated, assuming constant mastitis prevalence and constant total production of somatic cells. Such an exercise shows that an increase in FCM from 3000 to 4000 kg would reduce the BMCG by about 25%, an increase from 5000 to 6000 kg would reduce it by about 17%, and an increase from 8000 to 9000 kg would reduce it by about 11 %. The effect of 'dilution' estimated in our analysis (11.1%) is, therefore, less than 'expected' for milk yields up to about 8000 kg of FCM if the effect of milk yield had been only due to dilution. This means that an increase in the milk production also increases the total number of somatic cells in the milk. It would seem that this effect is more pronounced at lower levels of milk pro-

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duction. An analysis was also made on the total number of cells produced, and it showed accordingly that the increase in number of cells with increasing milk yield, estimated conditionally on mastitis prevalence, diminished in inverse proportion to increasing level of production (the quadratic term was significant). Our estimate of the dilution effect is greater than expected for milk yields over 9000 kg of FCM. However, the results may not be very valid at this range because only 1% of the material had an average milk yield above 8480 kg of FCM (Table 1). One obstacle encountered in a study like this is to establish the true prevalence of mastitis in the herds. We used the procedure outlined by Brolund (3, 4) based on individual cow SCC in order to estimate the prevalence. When interpreting the results, consider that this estimate contains a degree of lDlcertainty because Brolund's models explained only 64% of the overall variation in the true prevalence (3). However, informal evaluation and practical experience have not given us any reason to think that there is a systematic influence on this uncertainty that would introduce a bias into our estimates but rather that it adds to the residual variation. CONCLUSIONS

The results of this study show that a high yielding herd can have higher PREY than a low yielding herd at the same average BMC. These results also imply that at least part of the decrease in the average BMC achieved during recent years may be an artifact due to the concurrent increase in milk production. The average annual milk yield in Sweden has increased by about 2000 kg of FCM since 1973, whereas the average cell count has decreased from about 340,000 to 220,000 cells/ml. About half of this decrease in BMC could, according to the results presented in this paper, be attributable to the increase in milk yield These results should perhaps be taken into account when comparing figures from different countries and changes in the BMC level. Another conclusion is that there does not seem to be a great deal of difference between geometric and arithmetic averages of BMC or between weighted and nonweighted averages with respect to the correlation with PREVo

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